noting that any (\(\theta ^*, c_j^*\) ) which satisfy () automatically satisfy (REF ). The conditions specified in () and () place further restrictions on the choice of \(\theta ^*\)  and \(c_j^*\) . Specifically () states that the optimal values must be concurrently chosen to produce a stationary point in the surrogate objective. For this we employ two processes. First, standard stochastic gradient ascent is applied to the surrogate objective (REF ) to adjust parameters \(\theta \) . Second, at each step of stochastic gradient ascent, the coefficients \(c_j\)  are adapted according to a method developed in [1]}The method developed in [1]} is for adapting a coefficient in KL divergence targeting. We adopt the method nearly verbatim only replacing the definition of \(d_j\)  as appropriate. The parameters 1.5 and 2 were taken directly.:
